The present invention relates to the detection of artifacts in a video signal, and more particularly to the detection of Gaussian noise in video signals.
There are known methods of measuring Gaussian noise in video signals. One such method measures the noise as the amount of fluctuations in known flat parts of analog video waveforms. This method works, but it is not pertinent where such noise occurs in video chains that have both analog and digital/compression links. Particularly in that the flat portions of the video waveform outside the active video area may be lost in subsequent digitization and/or compression processing.
Another method is to deduce the amount of noise from the active video area by measuring the statistics of properties of picture edges from an edge histogram. This method works for high amount of Gaussian noise in video signals, but it does not work well when lower, but nevertheless perceivable, amounts of noise are contained in the video signal.
What is desired is a method and apparatus for the detection of Gaussian noise in the active video area of video signals.
Accordingly the present invention provides a method and apparatus for the detection of Gaussian noise in a video signal by decomposing an image from the video signal into best qualified blocks of relatively uniform luminance. An average standard deviation is calculated for the best qualified blocks, and then smoothed by temporal filtering over other images from the video signal. The filtered average standard deviation is calibrated against a scale of corresponding input noise levels to obtain the Gaussian noise in the video signal.
The objects, advantages and other novel features of the present invention are apparent from the following detailed description when read in conjunction with the appended claims and attached drawing.